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dc.contributor.authorMálek, Jiří
dc.contributor.authorTran, Van Quang
dc.date.accessioned2019-02-22T07:58:52Z
dc.date.available2019-02-22T07:58:52Z
dc.date.issued2018
dc.identifier.citationTrendy v podnikání = Business trends : vědecký časopis Fakulty ekonomické ZČU v Plzni. 2018, č. 1, roč. 8, s. 3-11.cs
dc.identifier.issn1805-0603
dc.identifier.urihttp://hdl.handle.net/11025/31015
dc.identifier.urihttps://drive.google.com/drive/folders/1YgHOEzRsuZ2jDDK3Tn6-f_WPkrXm-5Pp
dc.description.sponsorshipAuthor Jiří Málek acknowledges the financial support of Czech Science Foundation with grant GAČR 18-05244S “Innovative Approaches to Credit Risk Management” and Institutional support IP 100040/1020. Author Tran van Quang is grateful for the financial support of grant GAČR 18-05244S “Innovative Approaches to Credit Risk Management” of Czech Science Foundation.en
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherZápadočeská univerzita v Plznics
dc.relation.ispartofseriesTrendy v podnikánícs
dc.rights© Západočeská univerzita v Plznics
dc.subjectkryptoměnycs
dc.subjectalfa-stabilní distribucecs
dc.subjectnormální inverzní gaussovská distribucecs
dc.subjectsilné koncecs
dc.titleInvestments in cryptocurrencies: how risky are they?en
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe article analyzes the probability distribution of returns of the daily data of four cryptocurrencies (Bitcoin, Ethereum, Ripple,Litecoin). Alpha-stable distribution and normal inverse Gaussian distribution (NIG) are used as approximation of the empirical distribution of log-returns as they allow to capture the "power" tails. First basic information about all four cryptocurrencies are given, followed by definition of alpha-stable distribution and normal inverse Gaussian distributions which is special case of generalized hyperbolic distribution. These distributions are used to approximate empirical distributions of these cryptocurrencies. The difference between these two distributions is that the stable distribution can model heavier ends than the NIG (NIG has so called semi-heavy tails). The parameters are estimated using MLE (Maximum Likelihood Estimation) method, which has proved to be the most accurate one. First, we compare the empirical distribution of Bitcoin with NIG and alpha-stable distribution (the stable distribution appears to be much more accurate than the NIG). Then the only stable distribution is used and its parameters are searched for all four cryptocurrencies. α of all cryptocurrencies is close to one, which means that the probability distribution is similar to Cauchy one. The smallest α (and therefore the fattest tail) has Litecoin, followed by Ripple, Bitcoin, and the highest α of Ethereum. On the other hand, Ethereum has the highest sample volatility.en
dc.subject.translatedcryptocurrenciesen
dc.subject.translatedalpha-stable distributionen
dc.subject.translatednormal inverse Gaussian distributionen
dc.subject.translatedfat tailsen
dc.identifier.doihttps://doi.org/10.24132/jbt.2018.8.1.3_11
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Číslo 1 (2018)
Číslo 1 (2018)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Trendy v podnikání
  5. 2018
  6. Číslo 1 (2018)